Large Language Models Unpack Complex Political Opinions through Target-Stance Extraction

Anastasia Giachanou; Florian Kunneman; Javier Garcia-Bernardo; \"Ozg\"ur Togay

arxiv: 2603.23531 · v2 · pith:27I6IJ5Znew · submitted 2026-03-07 · 💻 cs.CL

Large Language Models Unpack Complex Political Opinions through Target-Stance Extraction

\"Ozg\"ur Togay , Javier Garcia-Bernardo , Florian Kunneman , Anastasia Giachanou This is my paper

Pith reviewed 2026-05-21 11:27 UTC · model grok-4.3

classification 💻 cs.CL

keywords target stance extractionlarge language modelspolitical discourse analysisstance detectionreddit postscomputational social sciencezero-shot prompting

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The pith

Large language models identify targets and stances in political posts as accurately as trained humans

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines whether large language models can carry out Target-Stance Extraction on detailed political discussions drawn from Reddit. This task requires locating the specific subject under discussion and determining the expressed opinion toward that subject, which supports finer analysis than broad partisan categories alone. The authors assembled a collection of 1,084 posts covering 138 distinct targets and tested multiple models with zero-shot, few-shot, and context-augmented prompts. Results indicate that the strongest models reach performance levels comparable to expert human annotators and hold steady even on posts where humans show low agreement. Such capability would allow researchers to scale up the study of intricate political views without heavy reliance on manual labeling.

Core claim

Large language models, when given suitable prompts, perform Target-Stance Extraction on r/NeutralPolitics posts at a level comparable to highly trained human annotators while remaining robust on posts that exhibit low inter-annotator agreement.

What carries the argument

Target-Stance Extraction (TSE), the combined identification of discussion targets and the stances taken toward them

Load-bearing premise

Posts from the r/NeutralPolitics subreddit and the 1,084-item dataset built from them adequately represent the range and complexity of political opinions in wider online discourse.

What would settle it

Re-running the same models on a dataset collected from a different political discussion forum or containing more polarized or ambiguous posts would reveal whether accuracy falls substantially below human annotator levels.

read the original abstract

Political polarization emerges from a complex interplay of beliefs about policies, figures, and issues. However, most computational analyses reduce discourse to coarse partisan labels, overlooking how these beliefs interact. This is especially evident in online political conversations, which are often nuanced and cover a wide range of subjects, making it difficult to automatically identify the target of discussion and the opinion expressed toward them. In this study, we investigate whether Large Language Models (LLMs) can address this challenge through Target-Stance Extraction (TSE), a recent natural language processing task that combines target identification and stance detection, enabling more granular analysis of political opinions. For this, we construct a dataset of 1,084 Reddit posts from r/NeutralPolitics, covering 138 distinct political targets and evaluate a range of proprietary and open-source LLMs using zero-shot, few-shot, and context-augmented prompting strategies. Our results show that the best models perform comparably to highly trained human annotators and remain robust on challenging posts with low inter-annotator agreement. These findings demonstrate that LLMs can extract complex political opinions with minimal supervision, offering a scalable tool for computational social science and political text analysis.

Editorial analysis

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Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper delivers a new 1,084-post Reddit dataset and shows LLMs can reach human-level target-stance extraction on it, but the neutral subreddit source limits how much this tests truly complex political talk.

read the letter

The paper gives a usable new dataset of 1,084 posts from r/NeutralPolitics that cover 138 political targets, then tests a range of LLMs on target-stance extraction with zero-shot, few-shot, and context prompts against human annotators. Top models come close to the humans and stay stable even on the low-agreement items. That empirical comparison is the concrete contribution, and the setup is straightforward enough to be reproducible from the numbers they report.

Referee Report

1 major / 2 minor

Summary. The paper introduces Target-Stance Extraction (TSE) to identify targets and stances in nuanced political opinions from online text, beyond coarse partisan labels. It builds a dataset of 1,084 posts from r/NeutralPolitics spanning 138 targets and benchmarks proprietary and open-source LLMs under zero-shot, few-shot, and context-augmented prompting, reporting that top models match highly trained human annotators and remain robust on low inter-annotator agreement items.

Significance. If the empirical results hold, the work supplies a concrete demonstration that LLMs can perform granular political opinion extraction with minimal supervision, offering a potentially scalable method for computational social science. Credit is due for the direct human-annotator comparison on a newly collected dataset, multiple prompting conditions, and explicit robustness checks on low-agreement posts. The r/NeutralPolitics curation, however, narrows the tested distribution relative to unmoderated political discourse.

major comments (1)

[§3 and §5] §3 (Dataset Construction) and §5 (Results): the evaluation rests entirely on 1,084 posts from r/NeutralPolitics, a subreddit whose rules enforce neutrality, evidence-based claims, and removal of partisan or emotional content. This produces a narrower range of targets, stances, and linguistic complexity than typical unmoderated forums. The claim that LLMs 'can extract complex political opinions' and 'remain robust on challenging posts' therefore requires either explicit qualification or supplementary evaluation on more polarized, sarcastic, or rapidly shifting sources to be load-bearing for the broader applicability stated in the abstract.

minor comments (2)

[Abstract and §4] Abstract and §4 (Evaluation Metrics): inter-annotator agreement statistics and precise metric definitions (e.g., exact F1 formulation or agreement threshold for 'low-IAA' items) are referenced at a high level; explicit numerical values and formulas should be stated in the main text or appendix for full reproducibility.
[§5.3] §5.3 (Prompting Strategies): the context-augmented condition is described only at the level of 'additional context'; a short example prompt or precise definition of what context is supplied would clarify the experimental contrast.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for this constructive comment on dataset scope and generalizability. We agree that r/NeutralPolitics produces a narrower distribution than unmoderated forums and will revise the manuscript to qualify our claims accordingly while preserving the value of the current evaluation.

read point-by-point responses

Referee: [§3 and §5] §3 (Dataset Construction) and §5 (Results): the evaluation rests entirely on 1,084 posts from r/NeutralPolitics, a subreddit whose rules enforce neutrality, evidence-based claims, and removal of partisan or emotional content. This produces a narrower range of targets, stances, and linguistic complexity than typical unmoderated forums. The claim that LLMs 'can extract complex political opinions' and 'remain robust on challenging posts' therefore requires either explicit qualification or supplementary evaluation on more polarized, sarcastic, or rapidly shifting sources to be load-bearing for the broader applicability stated in the abstract.

Authors: We acknowledge that r/NeutralPolitics' moderation rules for evidence-based and non-partisan content result in a dataset with reduced sarcasm, emotional language, and rapid polarization compared to unmoderated sources. This was a deliberate choice to obtain reliable human annotations and high-quality ground truth for the 138 targets. The core empirical result—that top LLMs match trained annotators and remain robust on low-agreement items—still holds within this distribution. To make the broader claims load-bearing, we will (1) revise the abstract to qualify applicability (e.g., 'in evidence-based political discussions from moderated forums'), (2) expand the limitations discussion in the final section to explicitly note differences with more polarized or sarcastic sources, and (3) suggest future work on such corpora. We do not add new experiments, as that would exceed the scope of a minor revision. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical evaluation on independently annotated new dataset

full rationale

The paper constructs a fresh 1,084-post dataset from r/NeutralPolitics and reports LLM performance on Target-Stance Extraction via zero-shot/few-shot prompting, directly compared to human annotations produced independently of the models. No equations, fitted parameters, or self-citations are used to derive the accuracy figures; the central claims rest on external benchmarks (human IAA and model outputs on held-out posts) rather than reducing to the paper's own inputs by construction. This is a standard self-contained empirical study.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The claim rests on the representativeness of the r/NeutralPolitics sample and the reliability of human annotations as an external benchmark; no free parameters are fitted inside the reported results.

axioms (1)

domain assumption Human annotations produced by trained annotators constitute a reliable external gold standard for evaluating automated target-stance extraction.
The paper directly compares LLM outputs to these annotations to establish performance parity.

pith-pipeline@v0.9.0 · 5747 in / 1232 out tokens · 35690 ms · 2026-05-21T11:27:35.117896+00:00 · methodology

discussion (0)

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We evaluate ... LLMs using zero-shot, few-shot, and context-augmented prompting strategies. Our results show that the best models perform comparably to highly trained human annotators

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